Robotics-based location sensing using wireless ethernet
Proceedings of the 8th annual international conference on Mobile computing and networking
ARIADNE: a dynamic indoor signal map construction and localization system
Proceedings of the 4th international conference on Mobile systems, applications and services
COMPASS: A probabilistic indoor positioning system based on 802.11 and digital compasses
WiNTECH '06 Proceedings of the 1st international workshop on Wireless network testbeds, experimental evaluation & characterization
The Horus location determination system
Wireless Networks
An Accurate and Fast WLAN User Location Estimation Method Based on Received Signal Strength
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part III: ICCS 2007
Orientation-Aware Indoor Localization Path Loss Prediction Model for Wireless Sensor Networks
NBiS '08 Proceedings of the 2nd international conference on Network-Based Information Systems
Calibree: Calibration-Free Localization Using Relative Distance Estimations
Pervasive '08 Proceedings of the 6th International Conference on Pervasive Computing
Access Point Localization Using Local Signal Strength Gradient
PAM '09 Proceedings of the 10th International Conference on Passive and Active Network Measurement
Indoor localization without the pain
Proceedings of the sixteenth annual international conference on Mobile computing and networking
SpinLoc: spin once to know your location
Proceedings of the Twelfth Workshop on Mobile Computing Systems & Applications
A survey of indoor positioning systems for wireless personal networks
IEEE Communications Surveys & Tutorials
Survey of Wireless Indoor Positioning Techniques and Systems
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
ARIEL: automatic wi-fi based room fingerprinting for indoor localization
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
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Fingerprinting-based indoor localization involves building a signal strength radio map. This map is usually built manually by a person holding the mapping device, which results in orientation-dependent fingerprints due to signal attenuation by the human body. To offset this distortion, fingerprints are typically collected for multiple orientations, but this requires a high effort for large localization areas. In this paper, we propose an approach to reduce the mapping effort by modeling the WLAN signal attenuation caused by the human body. By applying the model to the captured signal to compensate for the attenuation, it is possible to generate an orientation-independent fingerprint. We demonstrate that our model is location and person independent and its output is comparable with manually created radio maps. By using the model, the WLAN scanning effort can be reduced by 75% to 87.5% (depending on the number of orientations).